No a k8s dev, but I feel like this is the answer. K8s isn't usually just scheduling pods round robin or at random. There's a lot of state to evaluate, and the problem of scheduling pods becomes an NP-hard problem similar to bin packing problem. I doubt the implementation tries to be optimal here, but it feels a computationally heavy problem.

In what way is it NP-hard? From what I can gather it just eliminates nodes where the pod wouldn't be allowed to run, calculates a score for each and then randomly selects one of the nodes that has the lowest score, so trivially parallelizable.