Which statement describes the advantages of using subset selection over least squares?

Using subset selection over least squares primarily offers the advantage of simplifying the model. Selecting a subset of the most relevant variables can enhance interpretability and reduce complexity by removing unnecessary predictors that do not contribute significantly to the model's predictive power. This simplification enables clearer inference and understanding of the relationships within the data.

By focusing on fewer variables, subset selection aids in avoiding issues related to overfitting that can arise with more complex models. When there are too many predictors, it becomes challenging to discern which variables are truly important, making decision-making more cumbersome. Therefore, simplifying the model not only improves interpretability but often leads to more reliable predictions by focusing on relevant factors.

In contrast, while improving model complexity might sound beneficial, it can lead to models that are too complex to be useful in practice. The claim about computational speed is also not universally true, as subset selection can sometimes be computationally intense, especially with larger datasets. Additionally, results from subset selection usually gain clarity in interpretation, making this the clear advantage of the approach.