Question for for Data Science for Python

Let x=[0, .1, .2, ...., .9, 1.0] and y=[0,0,0,0,0,1,1,1,1,1,1]. Fit the ordinary least square linear model (OLS), the Ridge model (RIDGE) with alpha=0.5, and the logistic regression model (LR) into these data and calculate the fitting score for each of the models. What is the relationships between the models' scores?

a.		score(RIDGE) < score(OLS) < score(LR)
b.		score(OLS) < score(RIDGE) < score(LR)
c.		score(LR) < score(RIDGE) < score(OLS)
d.		score(LR) < score(OLS) < score(RIDGE)