Explaining negative R-squared

When I first started out doing machine learning, I learnt that:

• R² is the coefficient of determination, a measure of how well is the data explained by the fitted model,
• R² is the square of the coefficient of correlation, R,
• R is a quantity that ranges from 0 to 1

Therefore, R² should also range from 0 to 1.

Colour me surprised when the `r2_score` implementation in sklearn returned negative scores. What gives?

data science negative regression r-squared statistics