random_state parameter in machine learning

Question

what is the difference when i set the random_state of an estimator vs setting the random state of any one of model_selection. assume that the value is constant in this case.
Example:
kf = KFold(..., random_state=12)
model = DecisionTreeClassifier(random_state=12)

OR

xtrain, xtest, ytrain, ytest = train_test_split(..., random_state=12)
model = LinearRegression(..., random_state=12)

Answer 1

Usually, these functions require the usage of randomness for initialization of data or other processes. When the random_state is not set, then it is randomly chosen on execution. This doesn't allow for replicability, since everytime you execute the code it would use another random_state.

Manually setting a random state allows you to replicate a result, which is useful for debugging. There's really no other advantage to it.