Parameter penalties are techniques used in machine learning and statistical modeling to prevent overfitting by adding a regularization term to the loss function. These penalties impose a cost on the complexity of the model, encouraging simpler models that generalize better to unseen data. Common types of parameter penalties include L1 (Lasso), L2 (Ridge), and elastic-net, each with distinct effects on the model’s parameters. By tuning these penalties, practitioners can balance the trade-off between fitting the training data well and maintaining model simplicity, ultimately leading to improved predictive performance and robustness in various applications.