Which of the following scenarios best exemplifies the application of Empirical Risk Minimization in model training?

Empirical Risk Minimization means choosing a model that makes the average prediction error as small as possible on the data you actually have. In practice you compute a loss for every training example, average those losses, and adjust the model to reduce that average. For instance, a linear regression that minimizes the mean squared error over a set of points is doing empirical risk minimization. This approach directly ties the training objective to the data’s observed errors, not to any theoretical distribution. The key idea is that the best model is the one that performs best on the empirical data set.