Generalization techniques are methods used in machine learning and statistics to create models that can perform well on unseen data. These techniques help ensure that a model does not just memorize the training data but instead learns patterns that can be applied to new situations. Common approaches include cross-validation, regularization, and ensemble methods. By applying generalization techniques, practitioners can improve the robustness and accuracy of their models. For instance, cross-validation helps assess how the results of a statistical analysis will generalize to an independent data set, while regularization prevents overfitting by adding a penalty for complexity.