Query optimizers traditionally rely on cost models to choose the best execution plan, and while machine learning-based cost models have been proposed to overcome weaknesses of traditional models, limited efforts have been made to investigate how well Learned Cost Models (LCMs) actually perform in query optimization and how they affect overall query performance. This paper presents a systematic study that evaluates LCMs on three core query optimization tasks: join ordering, access path selection, and physical operator selection.