How to Bootstrap dataset for 10000 AUC scores? I am new to ML and trying to learn the nuances. I work on a binary classification problem with 5K records. Label 1 is 1554 and Label 0 is 3554.

What I currently do is 

1) split the data into train(70%) and test(30%)

2) initiate a model -->  logreg=LogisticRegression(random_state=41)

3) run 10 fold cv -->   logreg_cv=GridSearchCV(logreg,op_param_grid,cv=10,scoring='f1')

4) fit the model -->  logreg_cv.fit(X_train_std,y_train)

5) Do prediction  -->  y_pred = logreg_cv.predict(X_test_std)

Now my question is, how to generate 10000 AUC scores. 

I read that people usually do this get a confidence interval of their train and test performance AUC scores.

So, I would like to know how to do this?

I know that bootstrap means generate random samples with replacement from same dataset. But do we still have to split them into train and test? But this looks no different than CV. How do we generate 10000 AUC's and get a confidence interval? 

Can you help?
In this question of stats exchange you can see an answer to your question of when to use bootstrap over CV.

You can see a simple tutorial of how to do Bootstrap in Python in this link

How to generate 10k AUC Scores?
AUC is a performance metric and what you are going to measure is the performance of your model 10k times. For that, you have to select 10k times the number of samples that you consider and measure AUC

for i in range(0,10_000):
    sample = df.sample(df.shape[0]/10,random_state=i)
    X = df.drop(columns='target')
    y = df.target
    preds = logreg.predict(X)
    print(roc_auc_score(preds,y))