An ensemble approach in research involves combining the predictions of multiple individual models to improve overall accuracy and performance. This technique is widely used in machine learning and data mining to reduce errors and enhance the robustness of predictions. Ensemble methods can include averaging predictions, using weighted voting schemes, or combining different models like decision trees or neural networks. By leveraging the diversity of multiple models, ensemble approaches can often outperform any individual model and provide more reliable results.