K-Fold Cross-Validation
K-Fold Cross-Validation is a resampling technique used in machine learning to evaluate model performance by partitioning a dataset into K equally sized folds. It trains the model K times, each time using K-1 folds for training and the remaining fold for validation, then averages the results to provide a robust performance estimate. This method helps reduce variance and overfitting compared to simple train-test splits.
Developers should use K-Fold Cross-Validation when building machine learning models to ensure reliable performance metrics, especially with limited data, as it maximizes data usage and provides more stable estimates. It is essential for hyperparameter tuning, model selection, and avoiding overfitting in applications like predictive analytics, classification, and regression tasks.