Effective feature selection impacts machine learning models by simplifying the model training process. Feature selection involves choosing a subset of relevant features from the original set, focusing on those that contribute the most to the predictive performance of the model. By selecting the most informative features, the model training process becomes more efficient and less prone to overfitting, as irrelevant or redundant features are excluded. Reducing the number of features also helps mitigate the curse of dimensionality, making the model more interpretable and improving its generalization to new, unseen data. In summary, effective feature selection simplifies the model training process, enhances model interpretability, and often leads to better overall model performance.