Backward Elimination is a statistical method used in model selection to identify the most significant variables in a dataset. It starts with a full model that includes all potential predictors and systematically removes the least significant variables one at a time. This process continues until only the variables that contribute meaningfully to the model remain. The goal of Backward Elimination is to improve model performance and interpretability by focusing on the most relevant predictors. This technique is commonly used in fields like machine learning and regression analysis to enhance predictive accuracy while reducing complexity.