Why is cross-validation important for small datasets?

Example Concept: Cross-validation, particularly k-fold cross-validation, involves partitioning the dataset into k subsets or "folds." The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold serving as the validation set once. The results are averaged to produce a single estimation. This technique is especially useful for small datasets as it allows every data point to be used for both training and validation, providing a comprehensive evaluation of the model's performance.