The correct answer is: B. max, l1 and l2 norms.
The Normalizer class in scikit-learn can be used to normalize data by applying a variety of norms, including the max norm, the l1 norm, and the l2 norm. The max norm is the simplest norm, and it simply sets each element of the data to its maximum value. The l1 norm is also known as the Manhattan norm, and it is calculated by summing the absolute values of the differences between each element of the data and the mean of the data. The l2 norm is also known as the Euclidean norm, and it is calculated by taking the square root of the sum of the squares of the differences between each element of the data and the mean of the data.
The max, l1, and l2 norms are all commonly used norms for normalizing data. The max norm is the simplest to implement, but it is not very robust to outliers. The l1 norm is more robust to outliers than the max norm, but it is not as efficient to compute. The l2 norm is the most robust to outliers of the three norms, and it is also the most efficient to compute.
In general, the l2 norm is the preferred norm for normalizing data. However, the max norm or the l1 norm may be a better choice if the data contains a lot of outliers.