K-fold cross-validation is a technique to evaluate models by dividing the dataset into K equal parts, using one part for testing and the rest for training. Results are averaged for each fold. K is often chosen as 5 or 10 for effective data utilization and reduced variance. Note: stratified K-fold maintains class ratios; computation cost increases linearly with K