In model selection, Occam's Razor guides the choice of models with fewer parameters or simpler structures, as they are more likely to generalize well to unseen data. In feature selection, Occam's Razor encourages the use of a smaller number of relevant features, reducing the dimensionality of the data and making the model less complex. In hyperparameter tuning, Occam's Razor suggests selecting hyperparameter values that lead to simpler models, which can help prevent overfitting and improve generalization.