What is the purpose of K-fold cross-validation?

K-fold cross-validation is primarily used to evaluate a model's performance more reliably and comprehensively. This technique involves dividing the dataset into 'K' subsets, or folds. The model is then trained on 'K-1' folds and tested on the remaining fold. This process is repeated 'K' times, with each fold serving as the test set once. By aggregating the performance metrics across all iterations, K-fold cross-validation provides a more stable and unbiased estimate of the model's generalization capabilities when applied to unseen data.

This method is particularly advantageous because it maximizes the use of the available data, as every instance is used for both training and validation, leading to a better understanding of how the model is expected to perform in real-world scenarios. It helps in identifying issues like overfitting or underfitting and makes it easier to compare the effectiveness of different models or hyperparameter configurations.

The other choices are relevant to different aspects of the machine learning process but do not capture the main objective of K-fold cross-validation. For instance, feature engineering relates to the transformation and selection of features used in model training, while simplifying the model structure refers to reducing complexity for better interpretability. Eliminating data leakage is a separate concern regarding how the data