Cross-validation estimates how well a ML model generalises to new, unseen data. Essential!
Avoid overfitting! CV gives realistic performance. Tune hyperparameters accurately.
Popular choice. Good balance: computation vs. reliability. Repeat several times.
Ensures each fold has similar class distribution. Ideal for imbalanced datasets.
Key! Properly split data before feature scaling. Prevent biased evaluation!
Essential for building robust, generalisable machine learning models. Start today!