A standard cluster is preferred over a single-node cluster when the project involves heavy computation that can be parallelized, such as training large-scale machine learning models. This is because a standard cluster can distribute the workload across multiple nodes, leading to faster processing times. On the other hand, a single-node cluster is preferred when the project requires high interactivity and low latency, such as during the exploratory data analysis phase, where the overhead of distributed processing is not necessary.