Ensemble learning is a machine learning technique where multiple models are combined to improve the overall predictive performance. These models can be diverse in nature, such as decision trees, neural networks, or support vector machines. Ensemble learning works by combining the predictions of multiple models to create a more accurate and robust final prediction. This approach often leads to better results than using a single model alone, as the aggregated predictions of diverse models can compensate for each other's weaknesses and biases. Ensemble learning is widely used in a variety of applications, including classification, regression, and anomaly detection tasks.