Walk Forward Validation (WFV) involves a training window that moves forward in time, training the model on historical data and then validating it on future, unseen data points. Unlike traditional cross-validation where data is randomly split, WFV respects the sequence of time, making it ideal for datasets with time-dependent features like stock prices, weather patterns, or sales figures.