What key benefit does cross-validation provide in model evaluation?

Cross-validation primarily helps in improving model accuracy on unseen data. It achieves this by dividing the dataset into multiple subsets or folds, allowing the model to be trained and validated on different portions of the data. This process enables a more robust assessment of how the model will perform when exposed to new, unseen data.

By using various training and testing splits, cross-validation mitigates the risk of overfitting to a specific subset of the data. This means that the model is less likely to memorize the training data, which can result in poor performance when generalizing to new examples. Instead, it focuses on capturing the underlying patterns present in the data that apply broadly.

The benefit lies in the model's enhanced ability to generalize, leading to better performance when confronted with real-world data or elements not present in the training set. Therefore, cross-validation contributes significantly to estimating the model's accuracy in practical applications.

Regarding the other options, reducing training time isn't a focus of cross-validation, as the process may even increase the overall training time due to the repetition of training on multiple folds. Moreover, while cross-validation improves the assessment of model performance, it does not eliminate the need for separate testing data; testing data remains important to confirm the model's efficacy. Lastly