Which of the following is a key benefit of using ensemble methods in machine learning?

Ensemble methods in machine learning are techniques that combine the predictions from multiple models to produce a more accurate and robust overall prediction. This approach leverages the strengths of various algorithms, making it possible to achieve higher accuracy than any individual model could provide on its own. By aggregating the outputs of multiple classifiers, ensemble methods can effectively reduce the likelihood of overfitting and enhance generalization to unseen data.

The power of ensemble methods lies in their ability to smooth out individual model biases and variances by pooling their predictions. For example, techniques such as bagging (e.g., Random Forests) and boosting (e.g., AdaBoost) exemplify how different algorithms can be merged to improve predictive performance.

In contrast, the other choices suggest benefits that do not accurately reflect the role of ensemble methods: they do not inherently reduce the need for data preprocessing, they generally require more computational power due to managing multiple models, and they are not limited to linear models as they can be applied to various model types, including both linear and non-linear ones.