When we use logistic regression, we use cross entropy as the loss function. However, based on my understanding and https://machinelearningmastery.com/cross-entropy-for-machine-learning/, cross entropy evaluates if two or more distributions are similar to each other. And the distributions are assumed to be Bernoulli or Multinoulli.
So, my question is: why we can always use cross entropy, i.e., Bernoulli in regression problems? Does the real values and the predicted values always follow such distribution?