Which statement is true in the context of evaluating metrics for machine learning algorithms?

Which statement is true in the context of evaluating metrics for machine learning algorithms?

A. A random classifier has AUC (the area under ROC curve) of 0.5

B. Using only one evaluation metric is sufficient

C. The F-score is always equal to precision

D. Recall of 1 (100%) is always a good result