DOC minimal docstring fix + UG for feature selection (scikit-learn#16810)

Author: NicolasHug

doc/modules/feature_selection.rst

@@ -152,7 +152,8 @@ The features are considered unimportant and removed, if the corresponding
 ``threshold`` parameter. Apart from specifying the threshold numerically,
 there are built-in heuristics for finding a threshold using a string argument.
 Available heuristics are "mean", "median" and float multiples of these like
-"0.1*mean".
+"0.1*mean". In combination with the `threshold` criteria, one can use the
+`max_features` parameter to set a limit on the number of features to select.
 
 For examples on how it is to be used refer to the sections below.
 

sklearn/feature_selection/_from_model.py

@@ -116,9 +116,8 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
         estimator is of dimension 2.
 
     max_features : int or None, optional
-        The maximum number of features selected scoring above ``threshold``.
-        To disable ``threshold`` and only select based on ``max_features``,
-        set ``threshold=-np.inf``.
+        The maximum number of features to select.
+        To only select based on ``max_features``, set ``threshold=-np.inf``.
 
         .. versionadded:: 0.20