2. How do you explain cross validation? Cross-validation is a statistical method used to estimate the performance (or accuracy) of machine learning models. It is used to protect against overfitting in a predictive model, particularly in a case where the amount of data may be limited.

Is cross-validation always necessary?

In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter.

Why is cross-validation better than validation?

Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data. Hold-out, on the other hand, is dependent on just one train-test split.