Cross-validation
Split ML data into K pieces — train on K-1, validate on 1, rotate. More robust than a single train/test split.
K-fold (most common K=5 or 10), leave-one-out, stratified (preserves class balance). Helps determine whether a model generalises or just memorises. Standard practice in classic ML; less common in LLM training where data is too large.