StandardScaler is a preprocessing tool used in machine learning to standardize features by removing the mean and scaling to unit variance. This means that it transforms the data so that each feature has a mean of 0 and a standard deviation of 1. This is important because many algorithms perform better or converge faster when features are on a similar scale. Using StandardScaler helps to ensure that no single feature dominates the learning process due to its scale. It is particularly useful for algorithms that rely on distance calculations, such as k-nearest neighbors and support vector machines, as it improves their performance and accuracy.