How to get early stopping for lasso regression

I have a problem. Is there an option to get early stopping? Because I saw on a plot that I get Overfitting after a while, so I want to get the most optimal.

dfListingsFeature_regression = pd.read_csv(r"https://raw.githubusercontent.com/Coderanker3/dataset4/main/listings_cleaned.csv")
d = {True: 1, False: 0, np.nan : np.nan} 
dfListingsFeature_regression['host_is_superhost'] = dfListingsFeature_regression[
                                                             'host_is_superhost'].map(d).astype('int')

X = dfListingsFeature_regression.drop(columns=['host_id', 'id', 'price']) # Features
y = dfListingsFeature_regression['price'] # Target variable
print(dfListingsFeature_nor.shape)


steps = [('feature_selection', SelectFromModel(estimator=LogisticRegression(max_iter=1000))),
         ('lasso', Lasso(alpha=0.1))]

pipeline = Pipeline(steps) 

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=30)


parameteres = { }

grid = GridSearchCV(pipeline, param_grid=parameteres, cv=5)                
grid.fit(X_train, y_train)
                    
print("score = %3.2f" %(grid.score(X_test,y_test)))
print('Training set score: ' + str(grid.score(X_train,y_train)))
print('Test set score: ' + str(grid.score(X_test,y_test)))

# Prediction
y_pred = grid.predict(X_test)

print("RMSE Val:", metrics.mean_squared_error(y_test, y_pred, squared=False))

y_train_predict = grid.predict(X_train)
print("Train:" , metrics.mean_squared_error(y_train, y_train_predict , squared=False))

r2 = metrics.r2_score(y_test, y_pred)
print(r2)

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