hyperparameter tuning ridge classifier

from sklearn.model_selection import train_test_split
from sklearn.linear_model import RidgeClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
import numpy as np


X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.25, random_state=42)
scaler = MinMaxScaler() 
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = RidgeClassifier()

param_grid = {'alpha' : [0.2,0.4,0.6], 'tol': [0.1,0.2],'solver'  : ['auto','svd','cholesky']}

gridsearchcv = GridSearchCV(model, param_grid, cv = 3, verbose=True, n_jobs=-1)
best_parameters = gridsearchcv.fit(X_train, y_train)
best_parameters