diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
index 2a06bc5d1ff91..b3f84498feca6 100644
--- a/doc/modules/linear_model.rst
+++ b/doc/modules/linear_model.rst
@@ -869,6 +869,10 @@ model, the probabilities describing the possible outcomes of a single trial
 are modeled using a `logistic function
 <https://en.wikipedia.org/wiki/Logistic_function>`_.
 
+For a visual example of how :class:`LogisticRegression` models a 
+classification problem using the logistic curve, see:
+:ref:`sphx_glr_auto_examples_linear_model_plot_logistic.py`.
+
 This implementation can fit binary, One-vs-Rest, or multinomial logistic
 regression with optional :math:`\ell_1`, :math:`\ell_2` or Elastic-Net
 regularization.
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index e4e12d1435d41..4352a7fb44320 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -1105,6 +1105,10 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
 
     For a comparison of the LogisticRegression with other classifiers see:
     :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
+
+    For a visual example of how :class:`LogisticRegression` models a
+    classification problem using the logistic curve, see:
+    :ref:`sphx_glr_auto_examples_linear_model_plot_logistic.py`.
     """
 
     _parameter_constraints: dict = {