Which statement is a CORRECT explanation of curse of dimensionality in machine learning?

a$)$ A machine learning model's performance tends to decrease when the number of records $($data points$)$ in the training set becomes too large.

b$)$ A machine learning model's generalization ability tends to increase with the number of training data points.

c$)$ The computation costs of training a machine learning model in high dimension are more expensive.

d$)$ A machine learning model's performance tends to decrease when the number of predictor variables becomes too large.