In a dataset with a mix of numerical and categorical features, ensemble techniques like stacking can help improve the performance of the machine learning model. Stacking involves combining the predictions of multiple models, each trained on a different preprocessing method for handling numerical and categorical features, such as encoding or normalization. This allows the ensemble to capture the different ways of dealing with mixed feature types and improve the model's accuracy. Bagging and boosting can also be used, but they do not specifically address the issue of mixed feature types. Therefore, the correct answer is C, as it directly addresses the use of ensemble techniques in this scenario.