The process of feature selection in machine learning involves identifying and selecting a subset of relevant variables to create efficient models. This process is crucial for improving model performance, reducing overfitting, and minimizing training time. Techniques used in feature selection include filter methods, which evaluate the statistical significance of features, wrapper methods, that assess subsets of features based on their predictive power, and embedded methods, which perform feature selection during model training. Proper feature selection can lead to simpler, more interpretable models and enhance overall accuracy.