Which snippet correctly performs feature scaling using StandardScaler and ensures the same scaling is applied to both training and testing sets?
from sklearn.preprocessing import StandardScalerscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)
from sklearn.preprocessing import StandardScalerscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.fit_transform(X_test)
from sklearn.preprocessing import StandardScalerscaler = StandardScaler()X_train_scaled = scaler.fit(X_train)X_test_scaled = scaler.transform(X_test)
from sklearn.preprocessing import MinMaxScalerscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)