Parameter norm penalties are techniques used in machine learning to regularize models, helping to prevent overfitting by constraining the values of model parameters. These penalties apply mathematical norms, such as L1 and L2, to the parameters during the training process. The L1 norm encourages sparsity in the model, often leading to some parameters being exactly zero, which can be useful for feature selection. In contrast, the L2 norm promotes smaller parameter values, effectively distributing the weight across all features. By incorporating these penalties, models can achieve better generalization to unseen data, enhancing their predictive performance.