How to handle ROC AUC Mean and ROC AUC STD returns as NaN

Question

I'm working on a classification ML model But I'm getting ROC AUC Mean and ROC AUC STD as NaN. I've tried SMOTE to resample the data. How to handle this issue?

This is my code

# Model Selection
models = []
models.append(('Logistic Regression', LogisticRegression(solver='liblinear', random_state = 0, class_weight='balanced')))
models.append(('SVC', SVC(kernel = 'linear', random_state = 0)))
models.append(('Kernel SVM', SVC(kernel = 'rbf', random_state = 0)))
models.append(('KNN', KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)))
models.append(('Gaussian NB', GaussianNB()))
models.append(('Decision Tree Classifier',DecisionTreeClassifier(criterion = 'entropy', random_state = 0)))
models.append(('Random Forest', RandomForestClassifier(n_estimators=100, criterion = 'entropy', random_state = 0)))
#Evaluating Model Results:
acc_results = []
auc_results = []
names = []
# set table to table to populate with performance results
col = ['Algorithm', 'ROC AUC Mean', 'ROC AUC STD',
       'Accuracy Mean', 'Accuracy STD']
model_results = pd.DataFrame(columns=col)
i = 0
# Evaluate each model using k-fold cross-validation:
for name, model in models:
    kfold = model_selection.KFold(n_splits=10)
# accuracy scoring:
    cv_acc_results = model_selection.cross_val_score(
    model, X_train_resampled, y_train_resampled, cv=kfold, scoring='accuracy')
    # roc_auc scoring:
    cv_auc_results = model_selection.cross_val_score(
    model, X_train_resampled, y_train_resampled, cv=kfold, scoring='roc_auc')
    acc_results.append(cv_acc_results)
    auc_results.append(cv_auc_results)
    names.append(name)
    model_results.loc[i] = [name,
                            round(cv_auc_results.mean()*100, 2),
                            round(cv_auc_results.std()*100, 2),
                            round(cv_acc_results.mean()*100, 2),
                            round(cv_acc_results.std()*100, 2)
                            ]
    i += 1

model_results.sort_values(by=['ROC AUC Mean'], ascending=False)

This is the output returned like. PFA screenshot Output is here