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What is the main benefit of using parallel processing for hyperparameter tuning in machine learning?
A
It guarantees the same results every time.
B
It makes hyperparameter tuning unnecessary.
C
It accelerates the tuning by testing several configurations at once, which is particularly useful for extensive search areas or models that require heavy computation.
D
It makes the tuning process less complex.
