Which of the following best describes model validation?

Model validation is best described as a process to estimate the model's performance on unseen data. This involves evaluating how well the model is likely to perform in real-world scenarios where it encounters new, previously unobserved data. By using techniques such as cross-validation, holdout validation, or other validation sets, the goal is to assess the model's generalization ability. This is crucial in building robust models that can make accurate predictions beyond the data they were trained on.

In contrast, model training focuses on fitting the model to the training dataset, where the model learns patterns and relationships in the data. While feature selection is an important part of the modeling process, it is not the primary concern of model validation. Additionally, ensuring that a model perfectly fits the training data can lead to overfitting, which reduces the ability of the model to generalize to unseen data. Therefore, the emphasis in model validation is on achieving a balance where the model can perform well on new data, rather than just fitting the training data perfectly.