Z-score normalization is a statistical method used to standardize data points in a dataset. It transforms the data into a format where the mean is 0 and the standard deviation is 1. This is achieved by subtracting the mean of the dataset from each data point and then dividing by the standard deviation. This process allows for easier comparison of data points across different scales. This technique is particularly useful in fields like machine learning and data analysis, where it helps to ensure that features contribute equally to the results. By normalizing the data, it reduces the impact of outliers and allows algorithms to perform better.