Which technique involves scaling features so that the mean value is 0 and the standard deviation is 1?

The technique that involves scaling features so that the mean value is 0 and the standard deviation is 1 is indeed standardization. This process transforms the data by removing the mean and scaling to unit variance. The goal of standardization is to center the distribution of the data by subtracting the mean and then to scale it by dividing by the standard deviation. This is particularly useful for algorithms that assume the data is normally distributed and when measurements from different scales need to be compared.

Normalization, on the other hand, typically refers to the process of adjusting values in a dataset to a common scale, often within the range of 0 to 1, and is not focused specifically on the mean and standard deviation. Feature selection is the process of selecting a subset of relevant features for model construction, while data wrangling encompasses the broader practices of cleaning and transforming raw data into a usable format. These concepts serve different purposes in data processing and do not directly relate to the scaling of features in the manner that standardization does.