Overfitting, which can be mitigated with techniques like regularization, emerges as the main challenge due to the increased complexity of models.

Underfitting, a scenario where the model is too simplistic to grasp the data's underlying patterns, becomes the primary issue as datasets grow.

The efficient management of computational resources becomes crucial as datasets and models expand in size and complexity, posing the main challenge.

The simplicity of model interpretation, though important, does not represent the primary challenge in scaling machine learning models.