Methods of building machine learning models I have seen pages where they mention 5 methods of building models.

1) All-in
2) Backward Elimination
3) Forward Selection
4) Bidirectional Elimination
5) Score Comparision


I usually implement a linear regression or any algorithm using sklearn

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X, y)
y_pred = lr.predict(X_test)


How can one implement those 5 methods of building models? 

Can anyone explain the importance of this and What is most commonly used one out of these?
Strictly speaking, those are not methods of "building" models, they are feature selection strategies.

Most of them are implemented in Scikit-learn.