I am getting the following error. "You should leave random_state to its default (None), or set shuffle=True."

Question

I'm trying to test several families, using different algorithms to see if any perform well. And I want to compare AUC with Standard Deviation using cross-validation with K-Fold.

X = pd.concat([X_train, X_test])
y = pd.concat([y_train, y_test])
from sklearn import model_selection
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import (
    LogisticRegression,
)
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import (
    KNeighborsClassifier,
)
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import (
    RandomForestClassifier,
)
import xgboost

for model in [
    DummyClassifier,
    LogisticRegression,
    DecisionTreeClassifier,
    KNeighborsClassifier,
    GaussianNB,
    SVC,
    RandomForestClassifier,
    xgboost.XGBClassifier,
]:
    cls = model()
    kfold = model_selection.KFold(
        n_splits=10, random_state=42
    )
    s = model_selection.cross_val_score(
        cls, X, y, scoring="roc_auc", cv=kfold
    )
    print(
        f"{model.__name__:22}  AUC: "
        f"{s.mean():.3f} STD: {s.std():.2f}"
    )

ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None), or set shuffle=True.

Thank you for your help