scikit-learn · glemaitre · Jul 6, 2021 · Jun 6, 2020 · Jun 7, 2020 · Jun 7, 2020
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -73,7 +73,6 @@
     "LabelSpreading",
     "LarsCV",
     "LassoCV",
-    "LassoLarsCV",
     "LatentDirichletAllocation",
     "LedoitWolf",
     "LinearSVR",

diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py
@@ -1630,7 +1630,7 @@ def fit(self, X, y):
         Returns
         -------
         self : object
-            returns an instance of self.
+            Returns an instance of self.
         """
         _normalize = _deprecate_normalize(
             self.normalize, default=True, estimator_name=self.__class__.__name__
@@ -1733,7 +1733,7 @@ class LassoLarsCV(LarsCV):
     Parameters
     ----------
     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).
 
@@ -1779,7 +1779,7 @@ class LassoLarsCV(LarsCV):
 
     max_n_alphas : int, default=1000
         The maximum number of points on the path used to compute the
-        residuals in the cross-validation
+        residuals in the cross-validation.
 
     n_jobs : int or None, default=None
         Number of CPUs to use during the cross validation.
@@ -1844,22 +1844,22 @@ class LassoLarsCV(LarsCV):
 
         .. versionadded:: 0.24
 
-    Examples
+    See Also
     --------
-    >>> from sklearn.linear_model import LassoLarsCV
-    >>> from sklearn.datasets import make_regression
-    >>> X, y = make_regression(noise=4.0, random_state=0)
-    >>> reg = LassoLarsCV(cv=5, normalize=False).fit(X, y)
-    >>> reg.score(X, y)
-    0.9993...
-    >>> reg.alpha_
-    0.3972...
-    >>> reg.predict(X[:1,])
-    array([-78.4831...])
+    lars_path : Compute Least Angle Regression or Lasso
+        path using LARS algorithm.
+    lasso_path : Compute Lasso path with coordinate descent.
+    Lasso : Linear Model trained with L1 prior as
+        regularizer (aka the Lasso).
+    LassoCV : Lasso linear model with iterative fitting
+        along a regularization path.
+    LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
+    LassoLarsIC : Lasso model fit with Lars using BIC
+        or AIC for model selection.
+    sklearn.decomposition.sparse_encode : Sparse coding.
 
     Notes
     -----
-
     The object solves the same problem as the LassoCV object. However,
     unlike the LassoCV, it find the relevant alphas values by itself.
     In general, because of this property, it will be more stable.
@@ -1869,9 +1869,18 @@ class LassoLarsCV(LarsCV):
     features are selected compared to the total number, for instance if
     there are very few samples compared to the number of features.
 
-    See Also
+    Examples
     --------
-    lars_path, LassoLars, LarsCV, LassoCV
+    >>> from sklearn.linear_model import LassoLarsCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(noise=4.0, random_state=0)
+    >>> reg = LassoLarsCV(cv=5, normalize=False).fit(X, y)
+    >>> reg.score(X, y)
+    0.9993...
+    >>> reg.alpha_
+    0.3972...
+    >>> reg.predict(X[:1,])
+    array([-78.4831...])
     """
 
     method = "lasso"