Can we use PCA for feature selection?

Feature selection refers to choosing a subset of the features from the complete set of features.

No, PCA is not used as a feature selection technique because we know that any Principal Component axis is a linear combination of all the original set of feature variables which defines a new set of axes that explain most of the variations in the data.

Therefore while it performs well in many practical settings, it does not result in the development of a model that relies upon a small set of the original features.