The Robust Scaler is a data preprocessing technique used in machine learning to standardize features. It transforms the data by removing the median and scaling it according to the interquartile range (IQR). This method is particularly useful for datasets with outliers, as it is less sensitive to extreme values compared to other scaling methods like the Standard Scaler. By using the Robust Scaler, the resulting data has a median of zero and an IQR of one. This ensures that the features are on a similar scale, which can improve the performance of various algorithms, including linear regression and support vector machines.