What is the role of cross-validation in model assessment?

Cross-validation plays a crucial role in evaluating how well a model will perform on unseen data by measuring its generalization ability. The primary objective of cross-validation is to ensure that the model not only fits the training data well but also maintains predictive accuracy when applied to new, independent datasets.

In a typical cross-validation procedure, the available dataset is divided into multiple subsets or "folds". The model is trained on some of these folds and tested on others. This iterative process provides several different assessments of the model's performance, allowing for a robust estimate of how it might behave in practical applications outside of the training data. By iterating through the data splits, cross-validation helps detect overfitting, where a model may perform exceptionally well on training data but poorly when generalized to new data.

While there are other aspects mentioned in the choices, such as assessing model complexity, validating assumptions, and calculating residuals, these do not directly capture the essence of cross-validation’s main objective—ensuring that the model generalizes well to additional data, which is encapsulated in the correct answer.