In a high-dimensional dataset with many irrelevant features, ensemble techniques like stacking can help improve the performance of the machine learning model. Stacking combines the predictions of multiple models, each trained on a different subset of features, allowing the ensemble to focus on the most relevant features and reduce the impact of irrelevant ones. Bagging and boosting can also be used, but they do not specifically address the issue of irrelevant features. Therefore, the correct answer is C, as it directly addresses the use of ensemble techniques in this scenario.