Feature Selection is a crucial process in Machine Learning that involves selecting a subset of relevant features for model training. By eliminating irrelevant or redundant data, it helps improve model performance and reduces overfitting, making the model more interpretable and efficient. This technique can be performed using various methods, such as filter, wrapper, and embedded approaches. Each method has its strengths and weaknesses, and the choice often depends on the specific dataset and problem at hand. Ultimately, effective feature selection can lead to better predictive accuracy and simpler models.