Z-Score Normalization is a statistical technique used to standardize data by transforming it into a common scale. This is achieved by calculating the mean and standard deviation of the dataset. Each data point is then adjusted by subtracting the mean and dividing by the standard deviation, resulting in a new value called the Z-score. The Z-score indicates how many standard deviations a data point is from the mean. A Z-score of 0 means the data point is exactly at the mean, while positive or negative values indicate how far and in which direction the point lies. This method is particularly useful in machine learning and data analysis for comparing different datasets.