Why is cross-validation important in model assessment?

Cross-validation is a crucial technique in model assessment because it plays a significant role in preventing overfitting. Overfitting occurs when a model learns the training data too well, capturing noise and outliers instead of the underlying patterns. This often leads to a model that performs excellently on the training data but poorly on unseen data.

By using cross-validation, data is split into different subsets multiple times, allowing the model to be trained and validated on various combinations of training and validation sets. This provides a more reliable estimate of a model's performance on new, unseen data. It helps ensure that the insights gained from the training process are generalizable rather than tailored to the specificities of the training dataset. Consequently, cross-validation gives a better assessment of how the model will perform in real-world situations, improving model robustness and accuracy.

In contrast, options discussing complexity and speed do not relate to the main advantage of cross-validation, which is to evaluate effectiveness in a way that guards against overfitting. The focus on comparison among models, while beneficial, is a secondary aspect; the primary reason for cross-validation is its role in ensuring the model's ability to generalize beyond the training data.