From 4aa6556257476a8625d7a65d6f2fa2ffd4c07057 Mon Sep 17 00:00:00 2001
From: spikebh <spikebh@gmail.com>
Date: Fri, 22 Oct 2021 16:14:05 -0300
Subject: [PATCH] DOC Ensures that OrthogonalMatchingPursuitCV passes numpydoc
 validation

---
 maint_tools/test_docstrings.py |  1 -
 sklearn/linear_model/_omp.py   | 31 ++++++++++++++++---------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index 3afc2b9e5cc18..6c9a71f1d05e3 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -15,7 +15,6 @@
 DOCSTRING_IGNORE_LIST = [
     "LabelSpreading",
     "MultiTaskElasticNetCV",
-    "OrthogonalMatchingPursuitCV",
     "PassiveAggressiveRegressor",
     "SpectralCoclustering",
     "SpectralEmbedding",
diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py
index 0dad398104143..8b94e4a32087f 100644
--- a/sklearn/linear_model/_omp.py
+++ b/sklearn/linear_model/_omp.py
@@ -880,7 +880,7 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):
         copy is made anyway.
 
     fit_intercept : bool, default=True
-        whether to calculate the intercept for this model. If set
+        Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
         (i.e. data is expected to be centered).
 
@@ -953,6 +953,20 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):
 
         .. versionadded:: 1.0
 
+    See Also
+    --------
+    orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
+    orthogonal_mp_gram : Solves n_targets Orthogonal Matching Pursuit
+        problems using only the Gram matrix X.T * X and the product X.T * y.
+    lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
+    Lars : Least Angle Regression model a.k.a. LAR.
+    LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
+    OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP).
+    LarsCV : Cross-validated Least Angle Regression model.
+    LassoLarsCV : Cross-validated Lasso model fit with Least Angle Regression.
+    sklearn.decomposition.sparse_encode : Generic sparse coding.
+        Each column of the result is the solution to a Lasso problem.
+
     Examples
     --------
     >>> from sklearn.linear_model import OrthogonalMatchingPursuitCV
@@ -966,19 +980,6 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):
     10
     >>> reg.predict(X[:1,])
     array([-78.3854...])
-
-    See Also
-    --------
-    orthogonal_mp
-    orthogonal_mp_gram
-    lars_path
-    Lars
-    LassoLars
-    OrthogonalMatchingPursuit
-    LarsCV
-    LassoLarsCV
-    sklearn.decomposition.sparse_encode
-
     """
 
     def __init__(
@@ -1014,7 +1015,7 @@ def fit(self, X, y):
         Returns
         -------
         self : object
-            returns an instance of self.
+            Returns an instance of self.
         """
 
         _normalize = _deprecate_normalize(