diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 4237d023973f7..ad2ccd15475f7 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -1232,6 +1232,43 @@ estimation.
    representations of feature space, also these approaches focus also on
    dimensionality reduction.
 
+.. _tree_ensemble_warm_start:
+
+Fitting additional trees
+------------------------
+
+RandomForest, Extra-Trees and :class:`RandomTreesEmbedding` estimators all support
+``warm_start=True`` which allows you to add more trees to an already fitted model.
+
+::
+
+  >>> from sklearn.datasets import make_classification
+  >>> from sklearn.ensemble import RandomForestClassifier
+
+  >>> X, y = make_classification(n_samples=100, random_state=1)
+  >>> clf = RandomForestClassifier(n_estimators=10)
+  >>> clf = clf.fit(X, y)  # fit with 10 trees
+  >>> len(clf.estimators_)
+  10
+  >>> # set warm_start and increase num of estimators
+  >>> _ = clf.set_params(n_estimators=20, warm_start=True)
+  >>> _ = clf.fit(X, y) # fit additional 10 trees
+  >>> len(clf.estimators_)
+  20
+
+When ``random_state`` is also set, the internal random state is also preserved
+between ``fit`` calls. This means that training a model once with ``n`` estimators is
+the same as building the model iteratively via multiple ``fit`` calls, where the
+final number of estimators is equal to ``n``.
+
+::
+
+  >>> clf = RandomForestClassifier(n_estimators=20)  # set `n_estimators` to 10 + 10
+  >>> _ = clf.fit(X, y)  # fit `estimators_` will be the same as `clf` above
+
+Note that this differs from the usual behavior of :term:`random_state` in that it does
+*not* result in the same result across different calls.
+
 .. _bagging:
 
 Bagging meta-estimator
diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index 6b1b842f5367b..28c404c3e406b 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -1308,7 +1308,7 @@ class RandomForestClassifier(ForestClassifier):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
             default=None
@@ -1710,7 +1710,7 @@ class RandomForestRegressor(ForestRegressor):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     ccp_alpha : non-negative float, default=0.0
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
@@ -2049,7 +2049,7 @@ class ExtraTreesClassifier(ForestClassifier):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
             default=None
@@ -2434,7 +2434,7 @@ class ExtraTreesRegressor(ForestRegressor):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     ccp_alpha : non-negative float, default=0.0
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
@@ -2727,7 +2727,7 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     Attributes
     ----------