Ensemble learning is a method used in machine learning where multiple models are trained to solve the same problem and then combined in a way that allows them to make more accurate predictions than any single model would. It can lead to a more robust model by reducing overfitting, improving accuracy, and providing a way to balance out the weaknesses of individual models.

Here’s a brief overview of some common ensemble learning techniques:

1. Bagging (Bootstrap Aggregating): This method involves creating multiple subsets of the original dataset through resampling and then training a model on each subset. The predictions are then combined through voting or averaging.
2. Boosting: Unlike bagging, where the models are trained independently, boosting involves training models sequentially, with each model trying to correct the mistakes of the previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
3. Stacking: Stacking combines predictions from different models by training a meta-model (or blender) on these predictions. This can often yield even better results than simply averaging or voting.
4. Random Forest: A popular ensemble method that creates a forest of decision trees, where each tree is trained on a different subset of the data. The predictions are combined through majority voting.

The power of ensemble learning comes from the idea that combining the predictions from multiple models can lead to more accurate and stable predictions. Different models may have different strengths and weaknesses, and ensemble learning leverages these differences to create a more robust prediction.

One of the challenges in using ensemble learning is that it can be computationally intensive, particularly if a large number of models are being trained. It may also be more complex to interpret the final model, compared to a single, simpler model.

Overall, ensemble learning has been proven to be a powerful tool in a variety of machine learning tasks, ranging from classification to regression, and it is widely used in practice for improving prediction performance.