In a scenario with limited labeled data, ensemble techniques like bagging can be helpful in improving the performance of the machine learning model. By training multiple models on different subsets of the limited data, bagging can reduce the risk of overfitting and improve the generalization of the ensemble. Data augmentation and transfer learning are not ensemble techniques, but they can also be used in conjunction with ensemble methods to further enhance the model's performance. However, the question specifically asks about ensemble techniques, making option C the correct choice.