What is cross-validation?

Cross-validation is a technique to estimate model performance more reliably by using the data more efficiently. In k-fold cross-validation, the training data is split into k equal folds. The model is trained k times, each time using k-1 folds for training and 1 fold for validation. The final performance is averaged across all k runs. This reduces variance in performance estimates compared to a single train/val split and helps detect overfitting. A common choice is k=5 or k=10.