How can cross-validation improve the reliability of a model?

Cross-validation is a powerful technique used to assess the generalizability of a model. It improves the reliability of a model primarily by ensuring that the model is tested on unseen data, which is critical for evaluating its performance outside of the training dataset. When a model is trained and tested on the same dataset, it may perform well due to simply memorizing the training examples, a phenomenon known as overfitting. This does not provide a true reflection of how the model will perform in real-world scenarios on new data.

By employing cross-validation, the dataset is divided into multiple subsets, allowing the model to be trained on some subsets while being validated on others that it has not encountered during training. This process results in a more robust evaluation as it accounts for various data distributions and characteristics, thus providing a more accurate estimation of model performance. Therefore, option B highlights the essential aspect of cross-validation in enhancing the reliability of predictions made by the model.