Exploratory Data Analysis and selecting good predictor variables ?
This is an interesting but broad question.

Imagine PCA. You yse it for exploring the data embedded in lower dimensional space but the first $n$ principale components are also used as the features (after projection of data on them).

Or you use correlation analysis and remove (deselect) features with high correlation with an existing feature.

You calculate the variance of each feature abd low variances tell you that there is no infirmation in this feature.

You inspect feature distributions according to target to determine how much they contribute to the prediction.

And of course much more ...