It involves partitioning the data into k -folds of equal size, where the model is trained on k -1 folds and assessed on the remaining fold. The process is repeated k times, with each fold serving as the validation set once. The model's performance is obtained by averaging the performance scores from each fold. Cross-validation utilizes the entire dataset for both training and evaluation, providing a robust estimate of the model's performance and its ability to generalize to new data.