diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index e9ceca7fe0ea8..a01993b613d63 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -23,6 +23,11 @@
     "RidgeClassifier.fit",
     "RidgeClassifierCV.decision_function",
     "SGDClassifier.decision_function",
+    "VotingRegressor$",
+    "VotingRegressor.fit",
+    "VotingRegressor.transform",
+    "VotingRegressor.predict",
+    "VotingRegressor.score",
     "KernelDensity",
     "KernelDensity.fit",
     "KernelDensity.score",
diff --git a/sklearn/base.py b/sklearn/base.py
index b4cedffd9dab3..16ef7324d856f 100644
--- a/sklearn/base.py
+++ b/sklearn/base.py
@@ -369,7 +369,7 @@ class RegressorMixin:
     _estimator_type = "regressor"
 
     def score(self, X, y, sample_weight=None):
-        """Returns the coefficient of determination R^2 of the prediction.
+        """Return the coefficient of determination R^2 of the prediction.
 
         The coefficient R^2 is defined as (1 - u/v), where u is the residual
         sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
@@ -549,11 +549,13 @@ def fit_transform(self, X, y=None, **fit_params):
         y : numpy array of shape [n_samples]
             Target values.
 
+        **fit_params : Any number of parameters
+            Fit parameters.
+
         Returns
         -------
         X_new : numpy array of shape [n_samples, n_features_new]
             Transformed array.
-
         """
         # non-optimized default implementation; override when a better
         # method is possible for a given clustering algorithm
diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py
index bddf5f00b10af..d1074c6b4b65d 100644
--- a/sklearn/ensemble/_voting.py
+++ b/sklearn/ensemble/_voting.py
@@ -328,7 +328,7 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
 
     Parameters
     ----------
-    estimators : list of (string, estimator) tuples
+    estimators : list of (str, estimator) tuples
         Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
         of those original estimators that will be stored in the class attribute
         ``self.estimators_``. An estimator can be set to ``'drop'`` using
@@ -359,6 +359,10 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
 
         .. versionadded:: 0.20
 
+    See Also
+    --------
+    VotingClassifier: Soft Voting/Majority Rule classifier.
+
     Examples
     --------
     >>> import numpy as np
@@ -372,10 +376,6 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
     >>> er = VotingRegressor([('lr', r1), ('rf', r2)])
     >>> print(er.fit(X, y).predict(X))
     [ 3.3  5.7 11.8 19.7 28.  40.3]
-
-    See also
-    --------
-    VotingClassifier: Soft Voting/Majority Rule classifier.
     """
 
     def __init__(self, estimators, weights=None, n_jobs=None):
@@ -384,7 +384,7 @@ def __init__(self, estimators, weights=None, n_jobs=None):
         self.n_jobs = n_jobs
 
     def fit(self, X, y, sample_weight=None):
-        """ Fit the estimators.
+        """Fit the estimators.
 
         Parameters
         ----------
@@ -403,6 +403,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Returns self.
         """
         y = column_or_1d(y, warn=True)
         return super().fit(X, y, sample_weight)
@@ -438,7 +439,7 @@ def transform(self, X):
         Returns
         -------
         predictions
-            array-like of shape (n_samples, n_classifiers), being
+            Array-like of shape (n_samples, n_classifiers), being
             values predicted by each regressor.
         """
         check_is_fitted(self)