Rescaling is the process of adjusting the range or distribution of data to fit a specific scale or format. This technique is often used in data analysis and machine learning to ensure that different features contribute equally to the model's performance. By transforming data to a common scale, it helps improve the accuracy and efficiency of algorithms. Common methods of rescaling include min-max normalization and z-score standardization. Min-max normalization adjusts values to a range between 0 and 1, while z-score standardization centers the data around a mean of 0 with a standard deviation of 1. Both methods help in comparing and interpreting data more effectively.