diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
index a8b0e939c080d..4321c39b45e92 100644
--- a/sklearn/linear_model/tests/test_least_angle.py
+++ b/sklearn/linear_model/tests/test_least_angle.py
@@ -3,12 +3,10 @@
 import numpy as np
 import pytest
 from scipy import linalg
-
 from sklearn.base import clone
 from sklearn.model_selection import train_test_split
 from sklearn.utils._testing import assert_allclose
 from sklearn.utils._testing import assert_array_almost_equal
-from sklearn.utils._testing import assert_raises
 from sklearn.utils._testing import ignore_warnings
 from sklearn.utils._testing import TempMemmap
 from sklearn.utils.fixes import np_version, parse_version
@@ -96,8 +94,8 @@ def test_lars_path_gram_equivalent(method, return_path):
 def test_x_none_gram_none_raises_value_error():
     # Test that lars_path with no X and Gram raises exception
     Xy = np.dot(X.T, y)
-    assert_raises(ValueError, linear_model.lars_path, None, y, Gram=None,
-                  Xy=Xy)
+    with pytest.raises(ValueError):
+        linear_model.lars_path(None, y, Gram=None, Xy=Xy)
 
 
 def test_all_precomputed():
@@ -489,7 +487,9 @@ def test_lasso_lars_ic():
 
     # test error on unknown IC
     lars_broken = linear_model.LassoLarsIC('<unknown>')
-    assert_raises(ValueError, lars_broken.fit, X, y)
+
+    with pytest.raises(ValueError):
+        lars_broken.fit(X, y)
 
 
 def test_lars_path_readonly_data():
diff --git a/sklearn/linear_model/tests/test_omp.py b/sklearn/linear_model/tests/test_omp.py
index 3cbda003f0148..1d2eb6a239786 100644
--- a/sklearn/linear_model/tests/test_omp.py
+++ b/sklearn/linear_model/tests/test_omp.py
@@ -4,7 +4,6 @@
 import numpy as np
 import pytest
 
-from sklearn.utils._testing import assert_raises
 from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import ignore_warnings
@@ -33,16 +32,16 @@
 
 def test_correct_shapes():
     assert (orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5).shape ==
-                 (n_features,))
+            (n_features,))
     assert (orthogonal_mp(X, y, n_nonzero_coefs=5).shape ==
-                 (n_features, 3))
+            (n_features, 3))
 
 
 def test_correct_shapes_gram():
     assert (orthogonal_mp_gram(G, Xy[:, 0], n_nonzero_coefs=5).shape ==
-                 (n_features,))
+            (n_features,))
     assert (orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5).shape ==
-                 (n_features, 3))
+            (n_features, 3))
 
 
 def test_n_nonzero_coefs():
@@ -88,15 +87,14 @@ def test_unreachable_accuracy():
                           n_nonzero_coefs=n_features))
 
 
-def test_bad_input():
-    assert_raises(ValueError, orthogonal_mp, X, y, tol=-1)
-    assert_raises(ValueError, orthogonal_mp, X, y, n_nonzero_coefs=-1)
-    assert_raises(ValueError, orthogonal_mp, X, y,
-                  n_nonzero_coefs=n_features + 1)
-    assert_raises(ValueError, orthogonal_mp_gram, G, Xy, tol=-1)
-    assert_raises(ValueError, orthogonal_mp_gram, G, Xy, n_nonzero_coefs=-1)
-    assert_raises(ValueError, orthogonal_mp_gram, G, Xy,
-                  n_nonzero_coefs=n_features + 1)
+@pytest.mark.parametrize("positional_params", [(X, y), (G, Xy)])
+@pytest.mark.parametrize(
+    "keyword_params",
+    [{"tol": -1}, {"n_nonzero_coefs": -1}, {"n_nonzero_coefs": n_features + 1}]
+)
+def test_bad_input(positional_params, keyword_params):
+    with pytest.raises(ValueError):
+        orthogonal_mp(*positional_params, **keyword_params)
 
 
 def test_perfect_signal_recovery():
diff --git a/sklearn/linear_model/tests/test_theil_sen.py b/sklearn/linear_model/tests/test_theil_sen.py
index c670fc3979b80..125c89599af83 100644
--- a/sklearn/linear_model/tests/test_theil_sen.py
+++ b/sklearn/linear_model/tests/test_theil_sen.py
@@ -17,7 +17,7 @@
 from sklearn.linear_model import LinearRegression, TheilSenRegressor
 from sklearn.linear_model._theil_sen import _spatial_median, _breakdown_point
 from sklearn.linear_model._theil_sen import _modified_weiszfeld_step
-from sklearn.utils._testing import assert_almost_equal, assert_raises
+from sklearn.utils._testing import assert_almost_equal
 
 
 @contextmanager
@@ -209,19 +209,23 @@ def test_calc_breakdown_point():
 def test_checksubparams_negative_subpopulation():
     X, y, w, c = gen_toy_problem_1d()
     theil_sen = TheilSenRegressor(max_subpopulation=-1, random_state=0)
-    assert_raises(ValueError, theil_sen.fit, X, y)
+
+    with pytest.raises(ValueError):
+        theil_sen.fit(X, y)
 
 
 def test_checksubparams_too_few_subsamples():
     X, y, w, c = gen_toy_problem_1d()
     theil_sen = TheilSenRegressor(n_subsamples=1, random_state=0)
-    assert_raises(ValueError, theil_sen.fit, X, y)
+    with pytest.raises(ValueError):
+        theil_sen.fit(X, y)
 
 
 def test_checksubparams_too_many_subsamples():
     X, y, w, c = gen_toy_problem_1d()
     theil_sen = TheilSenRegressor(n_subsamples=101, random_state=0)
-    assert_raises(ValueError, theil_sen.fit, X, y)
+    with pytest.raises(ValueError):
+        theil_sen.fit(X, y)
 
 
 def test_checksubparams_n_subsamples_if_less_samples_than_features():
@@ -230,7 +234,8 @@ def test_checksubparams_n_subsamples_if_less_samples_than_features():
     X = random_state.normal(size=(n_samples, n_features))
     y = random_state.normal(size=n_samples)
     theil_sen = TheilSenRegressor(n_subsamples=9, random_state=0)
-    assert_raises(ValueError, theil_sen.fit, X, y)
+    with pytest.raises(ValueError):
+        theil_sen.fit(X, y)
 
 
 def test_subpopulation():