What term is used to describe a model that is deemed useful for its intended task?

The term used to describe a model that is considered useful for its intended task is "skillful." A skillful model effectively captures the underlying patterns in the data and provides accurate predictions or classifications for new, unseen data. This concept emphasizes the model's ability to generalize well, meaning it performs satisfactorily not just on the training dataset but also on test datasets, reflecting its utility in practical applications.

In contrast, overfitting refers to a situation where a model learns the training data too well, including its noise and outliers, which results in poor performance on new data. Underfitting occurs when a model is too simplistic to capture the data's underlying structure, leading to poor performance on both training and test datasets. Selection bias is a situation where certain individuals or groups are systematically excluded from the sample, leading to skewed or invalid results. Thus, "skillful" is the term that accurately reflects a model's effectiveness for the tasks it is designed to address.