diff --git a/doc/install.rst b/doc/install.rst
index ce7fa80647fe3..20cd565f40622 100644
--- a/doc/install.rst
+++ b/doc/install.rst
@@ -1,33 +1,22 @@
-.. _installation-instructions:
+.. _install_scikit_learn:
 
-=======================
 Installing scikit-learn
 =======================
 
-There are different ways to install scikit-learn:
-
-* :ref:`Install the latest official release <install_official_release>`. This
-  is the best approach for most users. It will provide a stable version
-  and pre-built packages are available for most platforms.
-
-* Install the version of scikit-learn provided by your
-  :ref:`operating system or Python distribution <install_by_distribution>`.
-  This is a quick option for those who have operating systems or Python
-  distributions that distribute scikit-learn.
-  It might not provide the latest release version.
-
-* :ref:`Building the package from source
-  <install_bleeding_edge>`. This is best for users who want the
-  latest-and-greatest features and aren't afraid of running
-  brand-new code. This is also needed for users who wish to contribute to the
-  project.
+Scikit-learn can be installed from PyPI (using pip) or from conda-forge (using
+conda). Both methods are fully supported and widely used. If you already use
+conda, conda-forge provides optimized packages for scientific computing. If you
+prefer pip, PyPI wheels are equally valid.
 
-
-.. _install_official_release:
+We recommend installing scikit-learn in an isolated environment to avoid
+conflicts with other packages. Below are basic instructions for common operating
+systems.
 
 Installing the latest release
 =============================
 
+To install scikit-learn, follow the instructions for your operating system:
+
 .. raw:: html
 
   <style>
@@ -36,14 +25,6 @@ Installing the latest release
       .install-instructions .sd-tab-set {
         --tab-caption-width: 20%;
       }
-
-      .install-instructions .sd-tab-set.tabs-os::before {
-        content: "Operating System";
-      }
-
-      .install-instructions .sd-tab-set.tabs-package-manager::before {
-        content: "Package Manager";
-      }
     }
   </style>
 
@@ -60,76 +41,51 @@ Installing the latest release
 
         .. tab-item:: pip
           :class-label: tab-6
-          :sync: package-manager-pip
 
-          Install the 64-bit version of Python 3, for instance from the
-          `official website <https://www.python.org/downloads/windows/>`__.
+          1. Download and install the 64-bit Python 3 from the
+             `Windows official website <https://www.python.org/downloads/windows/>`_.
 
-          Now create a `virtual environment (venv)
-          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
-          Note that the virtual environment is optional but strongly recommended, in
-          order to avoid potential conflicts with other packages.
+          2. Create a virtual environment (recommended):
 
-          .. prompt:: powershell
+             .. prompt:: powershell
 
-            python -m venv sklearn-env
-            sklearn-env\Scripts\activate  # activate
-            pip install -U scikit-learn
+                python -m venv sklearn-env
+                sklearn-env\Scripts\activate
 
-          In order to check your installation, you can use:
+          3. Install scikit-learn using pip:
 
-          .. prompt:: powershell
+             .. prompt:: powershell
 
-            python -m pip show scikit-learn  # show scikit-learn version and location
-            python -m pip freeze             # show all installed packages in the environment
-            python -c "import sklearn; sklearn.show_versions()"
+                pip install -U scikit-learn
 
-        .. tab-item:: conda
-          :class-label: tab-6
-          :sync: package-manager-conda
+          4. Verify the installation:
 
-          .. include:: ./install_instructions_conda.rst
+             .. prompt:: powershell
 
-    .. tab-item:: MacOS
-      :class-label: tab-4
+                python -m pip show scikit-learn
+                python -c "import sklearn; sklearn.show_versions()"
 
-      .. tab-set::
-        :class: tabs-package-manager
-
-        .. tab-item:: pip
+        .. tab-item:: conda
           :class-label: tab-6
-          :sync: package-manager-pip
-
-          Install Python 3 using `homebrew <https://brew.sh/>`_ (`brew install python`)
-          or by manually installing the package from the `official website
-          <https://www.python.org/downloads/macos/>`__.
 
-          Now create a `virtual environment (venv)
-          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
-          Note that the virtual environment is optional but strongly recommended, in
-          order to avoid potential conflicts with other packges.
+          1. Install Conda using the
+             `Miniforge installers <https://github.com/conda-forge/miniforge#download>`_.
 
-          .. prompt:: bash
+          2. Create a new Conda environment and install scikit-learn:
 
-            python -m venv sklearn-env
-            source sklearn-env/bin/activate  # activate
-            pip install -U scikit-learn
+             .. prompt:: powershell
 
-          In order to check your installation, you can use:
+                conda create -n sklearn-env -c conda-forge scikit-learn
+                conda activate sklearn-env
 
-          .. prompt:: bash
+          3. Verify the installation:
 
-            python -m pip show scikit-learn  # show scikit-learn version and location
-            python -m pip freeze             # show all installed packages in the environment
-            python -c "import sklearn; sklearn.show_versions()"
-
-        .. tab-item:: conda
-          :class-label: tab-6
-          :sync: package-manager-conda
+             .. prompt:: powershell
 
-          .. include:: ./install_instructions_conda.rst
+                conda list scikit-learn
+                python -c "import sklearn; sklearn.show_versions()"
 
-    .. tab-item:: Linux
+    .. tab-item:: macOS
       :class-label: tab-4
 
       .. tab-set::
@@ -137,213 +93,152 @@ Installing the latest release
 
         .. tab-item:: pip
           :class-label: tab-6
-          :sync: package-manager-pip
 
-          Python 3 is usually installed by default on most Linux distributions. To
-          check if you have it installed, try:
+          1. Install Python 3 using `Homebrew <https://brew.sh>`_ or from the
+             `macOS official website <https://www.python.org/downloads/macos/>`_.
 
-          .. prompt:: bash
+          2. Create a virtual environment (recommended):
 
-            python3 --version
-            pip3 --version
+             .. prompt:: bash
 
-          If you don't have Python 3 installed, please install `python3` and
-          `python3-pip` from your distribution's package manager.
+                python3 -m venv sklearn-env
+                source sklearn-env/bin/activate
 
-          Now create a `virtual environment (venv)
-          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
-          Note that the virtual environment is optional but strongly recommended, in
-          order to avoid potential conflicts with other packages.
+          3. Install scikit-learn using pip:
 
-          .. prompt:: bash
+             .. prompt:: bash
 
-            python3 -m venv sklearn-env
-            source sklearn-env/bin/activate  # activate
-            pip3 install -U scikit-learn
+                pip install -U scikit-learn
 
-          In order to check your installation, you can use:
+          4. Verify the installation:
 
-          .. prompt:: bash
+             .. prompt:: bash
 
-            python3 -m pip show scikit-learn  # show scikit-learn version and location
-            python3 -m pip freeze             # show all installed packages in the environment
-            python3 -c "import sklearn; sklearn.show_versions()"
+                python3 -m pip show scikit-learn
+                python3 -c "import sklearn; sklearn.show_versions()"
 
         .. tab-item:: conda
           :class-label: tab-6
-          :sync: package-manager-conda
-
-          .. include:: ./install_instructions_conda.rst
-
-
-Using an isolated environment such as pip venv or conda makes it possible to
-install a specific version of scikit-learn with pip or conda and its dependencies
-independently of any previously installed Python packages. In particular under Linux
-it is discouraged to install pip packages alongside the packages managed by the
-package manager of the distribution (apt, dnf, pacman...).
 
-Note that you should always remember to activate the environment of your choice
-prior to running any Python command whenever you start a new terminal session.
+          1. Install Conda using the
+             `Miniforge installers <https://github.com/conda-forge/miniforge#download>`_.
 
-If you have not installed NumPy or SciPy yet, you can also install these using
-conda or pip. When using pip, please ensure that *binary wheels* are used,
-and NumPy and SciPy are not recompiled from source, which can happen when using
-particular configurations of operating system and hardware (such as Linux on
-a Raspberry Pi).
+          2. Create a new Conda environment and install scikit-learn:
 
-Scikit-learn plotting capabilities (i.e., functions starting with `plot\_`
-and classes ending with `Display`) require Matplotlib. The examples require
-Matplotlib and some examples require scikit-image, pandas, or seaborn. The
-minimum version of scikit-learn dependencies are listed below along with its
-purpose.
+             .. prompt:: bash
 
-.. include:: min_dependency_table.rst
+                conda create -n sklearn-env -c conda-forge scikit-learn
+                conda activate sklearn-env
 
-.. warning::
+          3. Verify the installation:
 
-    Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
-    Scikit-learn 0.21 supported Python 3.5-3.7.
-    Scikit-learn 0.22 supported Python 3.5-3.8.
-    Scikit-learn 0.23-0.24 required Python 3.6 or newer.
-    Scikit-learn 1.0 supported Python 3.7-3.10.
-    Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12
-    Scikit-learn 1.4 requires Python 3.9 or newer.
-
-.. _install_by_distribution:
-
-Third party distributions of scikit-learn
-=========================================
+             .. prompt:: bash
 
-Some third-party distributions provide versions of
-scikit-learn integrated with their package-management systems.
-
-These can make installation and upgrading much easier for users since
-the integration includes the ability to automatically install
-dependencies (numpy, scipy) that scikit-learn requires.
-
-The following is an incomplete list of OS and python distributions
-that provide their own version of scikit-learn.
-
-Alpine Linux
-------------
-
-Alpine Linux's package is provided through the `official repositories
-<https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn>`__ as
-``py3-scikit-learn`` for Python.
-It can be installed by typing the following command:
-
-.. prompt:: bash
-
-  sudo apk add py3-scikit-learn
-
-
-Arch Linux
-----------
-
-Arch Linux's package is provided through the `official repositories
-<https://www.archlinux.org/packages/?q=scikit-learn>`_ as
-``python-scikit-learn`` for Python.
-It can be installed by typing the following command:
-
-.. prompt:: bash
-
-  sudo pacman -S python-scikit-learn
-
-
-Debian/Ubuntu
--------------
-
-The Debian/Ubuntu package is split in three different packages called
-``python3-sklearn`` (python modules), ``python3-sklearn-lib`` (low-level
-implementations and bindings), ``python-sklearn-doc`` (documentation).
-Note that scikit-learn requires Python 3, hence the need to use the `python3-`
-suffixed package names.
-Packages can be installed using ``apt-get``:
-
-.. prompt:: bash
-
-  sudo apt-get install python3-sklearn python3-sklearn-lib python-sklearn-doc
+                conda list scikit-learn
+                python3 -c "import sklearn; sklearn.show_versions()"
 
+    .. tab-item:: Linux
+      :class-label: tab-4
 
-Fedora
-------
+      .. tab-set::
+        :class: tabs-package-manager
 
-The Fedora package is called ``python3-scikit-learn`` for the python 3 version,
-the only one available in Fedora.
-It can be installed using ``dnf``:
+        .. tab-item:: pip
+          :class-label: tab-6
 
-.. prompt:: bash
+          1. Ensure Python 3 and pip are installed:
 
-  sudo dnf install python3-scikit-learn
+             .. prompt:: bash
 
+                python3 --version
+                pip3 --version
 
-NetBSD
-------
+          2. Create a virtual environment (recommended):
 
-scikit-learn is available via `pkgsrc-wip <http://pkgsrc-wip.sourceforge.net/>`_:
-https://pkgsrc.se/math/py-scikit-learn
+             .. prompt:: bash
 
+                python3 -m venv sklearn-env
+                source sklearn-env/bin/activate
 
-MacPorts for Mac OSX
---------------------
+          3. Install scikit-learn using pip:
 
-The MacPorts package is named ``py<XY>-scikits-learn``,
-where ``XY`` denotes the Python version.
-It can be installed by typing the following
-command:
+             .. prompt:: bash
 
-.. prompt:: bash
+                pip3 install -U scikit-learn
 
-  sudo port install py39-scikit-learn
+          4. Verify the installation:
 
+             .. prompt:: bash
 
-Anaconda and Enthought Deployment Manager for all supported platforms
----------------------------------------------------------------------
+                python3 -m pip show scikit-learn
+                python3 -c "import sklearn; sklearn.show_versions()"
 
-`Anaconda <https://www.anaconda.com/download>`_ and
-`Enthought Deployment Manager <https://assets.enthought.com/downloads/>`_
-both ship with scikit-learn in addition to a large set of scientific
-python library for Windows, Mac OSX and Linux.
+        .. tab-item:: conda
+          :class-label: tab-6
 
-Anaconda offers scikit-learn as part of its free distribution.
+          1. Install Conda using the
+             `Miniforge installers <https://github.com/conda-forge/miniforge#download>`_.
 
+          2. Create a new Conda environment and install scikit-learn:
 
-Intel Extension for Scikit-learn
---------------------------------
+             .. prompt:: bash
 
-Intel maintains an optimized x86_64 package, available in PyPI (via `pip`),
-and in the `main`, `conda-forge` and `intel` conda channels:
+                conda create -n sklearn-env -c conda-forge scikit-learn
+                conda activate sklearn-env
 
-.. prompt:: bash
+          3. Verify the installation:
 
-  conda install scikit-learn-intelex
+             .. prompt:: bash
 
-This package has an Intel optimized version of many estimators. Whenever
-an alternative implementation doesn't exist, scikit-learn implementation
-is used as a fallback. Those optimized solvers come from the oneDAL
-C++ library and are optimized for the x86_64 architecture, and are
-optimized for multi-core Intel CPUs.
+                conda list scikit-learn
+                python3 -c "import sklearn; sklearn.show_versions()"
 
-Note that those solvers are not enabled by default, please refer to the
-`scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html>`_
-documentation for more details on usage scenarios. Direct export example:
+---
 
-.. prompt:: python >>>
+**Important**: Using an isolated environment such as ``pip venv`` or ``conda``
+ensures that scikit-learn and its dependencies are installed independently of
+other Python packages. Always activate the environment before running Python
+commands to avoid conflicts.
 
-  from sklearnex.neighbors import NearestNeighbors
+For more details on Python packaging and virtual environments, refer to the
+`PyPackaging Native documentation <https://py-pkgs.org>`_.
 
-Compatibility with the standard scikit-learn solvers is checked by running the
-full scikit-learn test suite via automated continuous integration as reported
-on https://github.com/intel/scikit-learn-intelex. If you observe any issue
-with `scikit-learn-intelex`, please report the issue on their
-`issue tracker <https://github.com/intel/scikit-learn-intelex/issues>`__.
 
+Third-party distributions of scikit-learn
+=========================================
 
-WinPython for Windows
----------------------
+Several third-party distributions include scikit-learn in their package
+managers. This can simplify installation and upgrading scikit-learn, as
+dependencies (NumPy, SciPy, etc.) are often handled automatically. Below is an
+incomplete list:
+
+- **Alpine Linux** (`py3-scikit-learn docs
+  <https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn>`_):
+  ``sudo apk add py3-scikit-learn``
+- **Arch Linux** (`python-scikit-learn
+  <https://archlinux.org/packages/?q=scikit-learn>`_):
+  ``sudo pacman -S python-scikit-learn``
+- **Debian/Ubuntu** (`python3-sklearn
+  <https://packages.debian.org/search?keywords=python3-sklearn>`_):
+  ``sudo apt-get install python3-sklearn python3-sklearn-lib python-sklearn-doc``
+- **Fedora** (`python3-scikit-learn
+  <https://apps.fedoraproject.org/packages/python3-scikit-learn>`_):
+  ``sudo dnf install python3-scikit-learn``
+- **NetBSD**: via `pkgsrc-wip <https://pkgsrc.se/math/py-scikit-learn>`_
+- **macOS (MacPorts)** (`py-scikits-learn
+  <https://ports.macports.org/port/py-scikits-learn/>`_):
+  ``sudo port install py39-scikit-learn``
+- **Intel Extension for scikit-learn** (`docs
+  <https://intel.github.io/scikit-learn-intelex>`_): install via
+  ``pip install scikit-learn-intelex`` or
+  ``conda install scikit-learn-intelex``
+- **WinPython for Windows** (`homepage
+  <https://winpython.github.io/>`_)
+
+.. note::
+   Third-party repositories may not always provide the latest release. Consult
+   each distributor’s documentation for more details.
 
-The `WinPython <https://winpython.github.io/>`_ project distributes
-scikit-learn as an additional plugin.
 
 
 Troubleshooting
@@ -351,37 +246,27 @@ Troubleshooting
 
 If you encounter unexpected failures when installing scikit-learn, you may submit
 an issue to the `issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_.
-Before that, please also make sure to check the following common issues.
-
-.. _windows_longpath:
+Before that, please check these common issues:
 
 Error caused by file path length limit on Windows
 -------------------------------------------------
 
-It can happen that pip fails to install packages when reaching the default path
-size limit of Windows if Python is installed in a nested location such as the
-`AppData` folder structure under the user home directory, for instance::
-
-    C:\Users\username>C:\Users\username\AppData\Local\Microsoft\WindowsApps\python.exe -m pip install scikit-learn
-    Collecting scikit-learn
-    ...
-    Installing collected packages: scikit-learn
-    ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\Users\\username\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'
-
-In this case it is possible to lift that limit in the Windows registry by
-using the ``regedit`` tool:
+Pip can fail to install packages when hitting the default path-size limit on
+Windows, especially if Python is installed under ``AppData``. For example::
 
-#. Type "regedit" in the Windows start menu to launch ``regedit``.
+   C:\Users\username>...
+   ERROR: Could not install packages due to an OSError: [Errno 2] No such file
+   or directory:
+   'C:\\Users\\username\\AppData\\Local\\...\\sklearn\\datasets\\tests\\data\\...'
 
-#. Go to the
-   ``Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem``
-   key.
+To fix this, enable long paths via the Windows registry:
 
-#. Edit the value of the ``LongPathsEnabled`` property of that key and set
-   it to 1.
+1. Type ``regedit`` in the Start menu.
+2. Go to ``Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem``.
+3. Edit the ``LongPathsEnabled`` property to ``1``.
 
-#. Reinstall scikit-learn (ignoring the previous broken installation):
+Then reinstall scikit-learn (ignoring the prior broken install):
 
-   .. prompt:: powershell
+.. prompt:: powershell
 
-      pip install --exists-action=i scikit-learn
+   pip install --exists-action=i scikit-learn