Cross-Validation

Cross-validation is a model evaluation technique that partitions a dataset into K equal folds and trains K models, each leaving one fold out as a validation set. Metrics are averaged across folds to produce a more reliable performance estimate than a single train/test split. It is especially valuable in NLP when labeled data is scarce, ensuring that evaluation results are not unduly influenced by a single favorable or unfavorable data partition. The most common variant is K-fold cross-validation with K between 5 and 10.