What happened:
I was running GridSearchCV with multiple scoring metrics. One of them ("neg_mean_poisson_deviance") was undefined for some folds b/c it is undefined when y_hat is 0. This was handled during scoring but when create_cv_results was called, this raised a TypeError: 'float' object is not subscriptable. This is b/c score would normally return a dictionary when mutliple scorers are requested but in this case it returned the value I had passed as error_score to GridSearchCV, which in this case was np.nan. The issue is between L274 and L297 in methods.py.

What you expected to happen:
I expected that score to be np.nan for the folds in which the scorer failed, but not to raise an error

Minimal Complete Verifiable Example:

from sklearn.linear_model import LinearRegression
from dask_ml.model_selection import GridSearchCV
from sklearn.model_selection import LeaveOneOut
import numpy as np

X = np.array([[1, 2],
              [2, 1],
              [0, 0]])

y = 3 * X[:, 0] + 4 * X[:, 1]
cv = LeaveOneOut()

ols = LinearRegression(fit_intercept=False)
regr = GridSearchCV(
    ols,
    {"normalize": [False, True]},
    scoring=["neg_mean_squared_error", "neg_mean_poisson_deviance"],
    refit=False,
    cv=cv,
    error_score=np.nan,
    n_jobs=1
)
regr.fit(X,y)

This gives the TypeError I mentioned

Anything else we need to know?:
I think this should be a fairly quick fix so I'm going to give it a try